ICRA 2012 Paper Abstract

Close

Paper WeB08.4

Leivas, Gabriel (Universidade Federal de Minas Gerais), Nascimento, Erickson (Universidade Federal de Minas Gerais (UFMG)), Wilson Vieira, Antonio (Universidade Federal de Minas Gerais), Campos, Mario Montenegro (Universidade Federal de Minas Gerais)

Sparse Spatial Coding: A Novel Approach for Efficient and Accurate Object Recognition

Scheduled for presentation during the Regular Session "Parts Handling and Manipulation" (WeB08), Wednesday, May 16, 2012, 11:15−11:30, Meeting Room 8 (Wacipi)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on November 18, 2017

Keywords Recognition, Computer Vision for Robotics and Automation

Abstract

Successful state-of-the-art object recognition techniques from images have been based on powerful methods, such as sparse representation, in order to replace the also popular vector quantization (VQ) approach. Recently, sparse coding, which is characterized by representing a signal in a sparse space, has raised the bar on several object recognition benchmarks. However, one serious drawback of sparse space based methods is that similar local features can be quantized into different visual words. We present in this paper a new method, called Sparse Spatial Coding (SSC), which combines a sparse coding dictionary learning, a spatial constraint coding stage and an online classification method to improve object recognition. An efficient new off-line classification algorithm is also presented. We overcome the problem of techniques which make use of sparse representation alone by generating the final representation with SSC and max pooling, presented for an online learning classifier. Experimental results obtained on the Caltech 101, Caltech 256, Corel 5000 and Corel 10000 databases, show that, to the best of our knowledge, our approach supersedes in accuracy the best published results to date on the same databases. As an extension, we also show high performance results on the MIT-67 indoor scene recognition dataset.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2017 PaperCept, Inc.
Page generated 2017-11-18  19:10:57 PST  Terms of use